This invention relates to a tool break detector system and to a method of monitoring cutting tool vibrations to detect tool breakage and to prevent false alarms on other vibration noise occurring during normal cutting.
Automation in the factory has advanced to a point where machining is often done without manual supervision. Tool breakage, however, has increasingly become a problem because a broken tool can damage an expensive workpiece if cutting is not quickly discontinued. In response to this problem, systems have been developed to monitor the status of the cutting tool, typically by the use of a sensor, such as an accelerometer, mounted near the tool in order to sense vibration and to automatically terminate cutting when tool breakage occurs. Tool breakage, or break event, detection is a problem of recognizing break patterns in the sensor signals and thus involves the extraction of pertinent features from the signals and then classifying the feature vector as either break or non-break. Prior art tool break detection systems handle this recognition problem through careful manual analysis of the available signals by which a set of heuristics is developed. These heuristics, or rules, are then translated into microprocessor based amplitude-time domain pattern recognition techniques for spotting those signal patterns which are found to accompany a tool break event.
An example of one such heuristic approach is U.S. Pat. No. 4,636,779, which discloses a tool break detection system wherein sampled vibration signals are digitized and processed via either digital circuitry or a programmable general purpose computer containing the logic for detecting break events. In the '779 patent, this digital circuitry or software calculates a running mean signal level of a selected number of signal samples. A transient detector compares every new sample with the running mean signal value of N previous samples to detect a transient or abrupt increase in signal level that may have its source in a major tool break event. A mean shift detector then compares the mean signal level after and before such a transient to detect a shift in mean level and thus a substantial change in background cutting noise. A mean shift persistence detector makes a check that the shift in mean level persists for a specified time period. Various other commonly assigned patents (U.S. Pat. Nos. 4,642,617; 4,636,780; 4,707,787; 4,724,524; 4,806,914; 4,849,741; 4,853,680; and 4,918,427) improve on and modify this algorithm for various reasons as explained therein, but all of these systems are alike in that expert developed heuristics are crucial to being able to discriminate normal cutting events from tool break events and these heuristics are never as accurate as one would like them to be.
The challenge of the tool break detection problem therefore lies in the inherently noisy and variable signals in which no simple characteristic can be found which accurately discriminates tool breakage from other normal cutting events. Another challenge presented by the detection problem is the relative scarcity of break data as compared with normal cutting data. Breaks occur randomly and infrequently so that capturing break data can be a costly and involved process. A break detection system which could provide improved detection sensitivity and lower false alarm rates and could be easily adapted for use with new tool types and cutting tasks would be desirable. Furthermore, since break data is scarce and difficult and costly to obtain especially for new tool types and cutting tasks, a system which does not depend on such break data would be especially desirable.